It is the best of times, it is the worst of times.

With computer based algorithms generating the bulk of the trades in today's electronic markets, the human trading patterns that technical analysis uncovers, has withered away.

Markets are now more apt to meander endlessly and then, suddenly, tick consistently up, or down, the price ladder, without a pause.

The behavioral patterns of today's markets no longer represent the ebb and flow of human traders fighting it out in the pits.

The behavioral patterns of today's markets are generated by trading algorithms that ingest gobs of data and they respond to this data within a millisecond - or less.

The markets march up the price ladder are caused by algorithms responding to other algorithms, which in turn trigger other algorithms.

Goldman Sach’s algorithms do not pause, flinch, or take a break.

The algorithms see the market moving up, they assess the available inventory of futures contracts or equity shares to deduce the level of imbalance in the supply and demand curve, and execute, execute, execute ... accordingly.

We as traders, do not have access to the magnificent algorithms that Goldman Sachs or Citadel or Renaissance Technologies possess.

It is our belief that we, as traders, must adapt and change in order to thrive in today’s markets.

The approach that has evolved to manage our trading activities in today’s electronic markets, is an approach that blends both man and machine, trader and computer, cerebral pattern recognition skills with automated execution.

We have dubbed this method, Hybrid Trading.

We do not aim to go head to head with Goldman Sachs or any other HFT shop.

Instead, we lever the capabilities of the human brain to organize and deploy algorithmic trading bots of our own.

These bots can be tuned for any market condition and deployed with the click of mouse or a keystroke.

Walter Isaacson, in his book review of Clive Thompson’s book, ‘Smarter Than You Think’ does a tremendous job of articulating the potential of a hybrid trading approach:

When the world chess champion Garry Kasparov was beaten in 1997 by Deep Blue, an I.B.M. supercomputer, it was considered to be a major milestone in the march toward artificial intelligence. It probably shouldn’t have been. As complex as chess is, it’s easy to see that its rules can be translated into algorithms so that computers, when they eventually got enough processing power, could crunch through billions of possible moves and past games. Deep Blue’s calculations were a fundamentally different process, most people would say, from the “real” thinking and intuition a human player would use.

Clive Thompson, a Brooklyn-based technology journalist, uses this tale to open “Smarter Than You Think,” his judicious and insightful book on human and machine intelligence. But he takes it to a more interesting level. The year after his defeat by Deep Blue, Kasparov set out to see what would happen if he paired a machine and a human chess player in a collaboration. Like a centaur, the hybrid would have the strength of each of its components: the processing power of a large logic circuit and the intuition of a human brain’s wetware. The result: human-machine teams, even when they didn’t include the best grandmasters or most powerful computers, consistently beat teams composed solely of human grandmasters or superfast machines.

Thompson’s point is that “artificial intelligence” — defined as machines that can think on their own just like or better than humans — is not yet (and may never be) as powerful as “intelligence amplification,” the symbiotic smarts that occur when human cognition is augmented by a close interaction with computers. When he played in collaboration with a computer, Kasparov said, it freed him to focus on the “creative texture” of the game. In the future, Thompson writes, we should not fear being beaten in chess by Deep Blue or in “Jeopardy!” by Watson. Instead, humans will find themselves working in partnership with the progeny of these supercomputers to diagnose diseases, solve crimes, write poetry and become (as the clever double meaning of the book’s title puts it) smarter than we think.

If this approach works for chess, and healthcare, via IBM’s Watson, why shouldn’t we aggressively apply it to trading?

Overcoming the Achilles Heel of Automated Trading

For quite some time traders have been able to buy or lease fully automated trading strategies.

How wonderful to be able to just buy a strategy, turn it on, let it run, and then head to the golf course, while your trading account accumulates algorithmically derived profits.

Except that is not the reality of fully automated trading strategies.

The reality of fully automated trading strategies is that they work well under some market conditions and degrade when the preferred market conditions dissipate.

For instance, a fully automated mean reversion trading strategy may work well in a sideways market, but once the market takes off, the reversion strategy will suffer a significant drawdown as it fights the prevailing trend.

The flip side of this is a fully automated trend strategy. This strategy may print money while a market is zooming up, but it will likely give it all, or even more, back, when the market churns.

Hybrid Trading overcomes this issue of matching an automated strategy with current market conditions by allowing a trader to deploy one or more bots that are tuned for the current market conditions.

If the market is meandering in small range, a Hybrid Trader can elect to deploy a bot that not only seeks out the coordinates where there price is likely to revert to the mean, but the bot will also only trigger an entry at these coordinates if, and when, the order book gets flushed out with enough ferocity to kick-start the mean reversion process.

Hence, a trader can instruct a bot to pass on an entry at a mean reversion location unless a simultaneous order flow event also occurs.

This ability for bots to require any combination of price action, market structure, and order flow rules in order to trade is a not only powerful combination, it can be executed effortlessly by bots with a millisecond precision.

If these conditions are met, the bot will engage in a trade. If not, the bot will wait until it is instructed to stand-down.

BotQuant can be configured for any trading style

Futures and forex trading contains substantial risk and is not for every investor. An investor couldpotentially lose all or more than the initial investment. Risk capital is money that can be lost withoutjeopardizing ones’ financial security or life style. Only risk capital should be used for trading and onlythose with sufficient risk capital should consider trading. Past performance is not necessarily indicative offuture results.

Hypothetical Performance Disclosure:

Hypothetical performance results have many inherent limitations, some of which are described below. no representation is being made that any account will or is likely to achieve profits or losses similar to those shown; in fact, there are frequently sharp differences between hypothetical performance results and the actual results subsequently achieved by any particular trading program. One of the limitations of hypothetical performance results is that they are generally prepared with the benefit of hindsight. In addition, hypothetical trading does not involve financial risk, and no hypothetical trading record can completely account for the impact of financial risk of actual trading. for example, the ability to withstand losses or to adhere to a particular trading program in spite of trading losses are material points which can also adversely affect actual trading results. There are numerous other factors related to the markets in general or to the implementation of any specific trading program which cannot be fully accounted for in the preparation of hypothetical performance results and all which can adversely affect trading results.